Somalia’s humanitarian situation remains “alarming” four years after a devastating famine with the number of people requiring emergency aid rising 17 per cent to more than 850,000 and those in “food-stressed” situations still at 2.3 million, according to the latest United Nations-managed food assessment study released today.

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The new European Union regulations for medical devices (EU MDRs) will have major implications for the labeling operations of every medical device manufacturer that trades in the EU. The regulations have far-reaching impact, and they are why labeling has recently become a mission-critical business system for medical device companies. Failure to produce accurate, MDR-compliant labeling may result in organizations that are no longer able to distribute their products in Europe, causing products to be unnecessarily stockpiled or even resulting in costly and embarrassing product recalls.
The needs for new symbology and additiona...

The abnormal growth of cells in the brain is known as brain tumor. A brain tumor is a kind of disease that can hit children, adults, and older adults. In this work, a proposed method for brain tumor detection and classification using MATLAB and based on magnetic resonance imaging plays an essential role in the brain-tumor disease diagnostic application that is based on manual and automatic detection. Moreover, various kinds of tumors exist so it is complicated to detect, and thus it is hard to make decisions. Correct segmentation and image enhancement give an accurate classification of brain tumor types. A probabilistic ne...

In this study, a classification method is proposed to classify normal and abnormal heart sound signals using random forests algorithm. The proposed framework was applied to a database of 100 heart sound signals which collected from the web site, firstly all the signals were processed using the wavelet technique to eliminate the noise from the signal, features were extracted from the enhanced signals and the most significant features was selected using the RFs. Finally the random forests classifier was used to perform the classification process. The system achieved 93.24% accuracy in distinguishing between normal and abnorm...